PyGeM: Python Geometrical Morphing

Tezzele, Marco; Demo, Nicola; Mola, Andrea; Rozza, Gianluigi

doi:10.1016/j.simpa.2020.100047

Cited by 24 publications

(9 citation statements)

References 12 publications

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“…Both the FFD and RBF algorithms briefly described in this section have been implemented in the Python library for geometrical morphing PyGeM [17], which has been used to produce all the deformed geometries and computational grids used in this work. An example of the RBF application to volumetric mesh morphing described in this paragraph is presented in Figure 4.…”

Section: How To Combine Different Shape Parametrization Strategiesmentioning

confidence: 99%

See 1 more Smart Citation

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Demo

Tezzele

Mola

et al. 2021

JMSE

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In the field of parametric partial differential equations, shape optimization represents a challenging problem due to the required computational resources. In this contribution, a data-driven framework involving multiple reduction techniques is proposed to reduce such computational burden. Proper orthogonal decomposition (POD) and active subspace genetic algorithm (ASGA) are applied for a dimensional reduction of the original (high fidelity) model and for an efficient genetic optimization based on active subspace property. The parameterization of the shape is applied directly to the computational mesh, propagating the generic deformation map applied to the surface (of the object to optimize) to the mesh nodes using a radial basis function (RBF) interpolation. Thus, topology and quality of the original mesh are preserved, enabling application of POD-based reduced order modeling techniques, and avoiding the necessity of additional meshing steps. Model order reduction is performed coupling POD and Gaussian process regression (GPR) in a data-driven fashion. The framework is validated on a benchmark ship.

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Section: How To Combine Different Shape Parametrization Strategiesmentioning

confidence: 99%

“…All the algorithms used in this work are implemented in open source software libraries [17][18][19][20], which we will briefly introduce in the discussions of the corresponding numerical methods. In Figure 1 we depicted an outline of the whole numerical pipeline we are going to present, emphasizing the methods and the softwares used.…”

Section: Introductionmentioning

confidence: 99%

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Demo

Tezzele

Mola

et al. 2021

JMSE

Self Cite

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“…Further investigation will involve the use of more active subspaces based fidelities, such as the kernel active subspaces [31]. This could also greatly improve data-driven non-intrusive reduced order methods [40][41][42][43] through modal coefficients reconstruction and prediction for parametric problems. We also mention the possible application to shape optimization problems for the evaluation of both the target function and the constraints.…”

Section: Discussionmentioning

confidence: 99%

Multi‐fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces

Romor

Tezzele

Rozza

2021

Proc Appl Math and Mech

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Gaussian processes are employed for non-parametric regression in a Bayesian setting. They generalize linear regression embedding the inputs in a latent manifold inside an infinite-dimensional reproducing kernel Hilbert space. We can augment the inputs with the observations of low-fidelity models in order to learn a more expressive latent manifold and thus increment the model's accuracy. This can be realized recursively with a chain of Gaussian processes with incrementally higher fidelity. We would like to extend these multi-fidelity model realizations to case studies affected by an high-dimensional input space but with a low intrinsic dimensionality. In this cases physical supported or purely numerical low-order models are still affected by the curse of dimensionality when queried for responses. When the model's gradients information is provided, the presence of an active subspace can be exploited to design low-fidelity response surfaces and thus enable Gaussian process multi-fidelity regression, without the need to perform new simulations. This is particularly useful in the case of data scarcity. In this work we present a multi-fidelity approach involving active subspaces and we test it on two different high-dimensional benchmarks.

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“…For morphing the geometry, PyGem, an open-source library in Python was used. We used Radial Basis Functions (RBF) in this package (14). We removed some chords from the original template (888 out of 4,971 total chordal elements).…”

Section: Geometrymentioning

confidence: 99%

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

Dabiri

Jiang

Mahadevan

et al. 2021

Front. Cardiovasc. Med.

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Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.

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PyGeM: Python Geometrical Morphing

Cited by 24 publications

References 12 publications

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Hull Shape Design Optimization with Parameter Space and Model Reductions, and Self-Learning Mesh Morphing

Multi‐fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces

Mitral Valve Atlas for Artificial Intelligence Predictions of MitraClip Intervention Outcomes

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